针对视频异常事件的时空相关性学习对检测性能存在重要影响的问题,提出了基于融合双支特征的带约束损失的视频异常检测方法(Dual-branch Feature Fusion Based Constrained Multi-loss Video Anomaly Detection,DBF-CML-transMIL)。该...针对视频异常事件的时空相关性学习对检测性能存在重要影响的问题,提出了基于融合双支特征的带约束损失的视频异常检测方法(Dual-branch Feature Fusion Based Constrained Multi-loss Video Anomaly Detection,DBF-CML-transMIL)。该方法考虑多示例学习中片段的显著性和相关性,利用多层线性神经网络学习各片段的空间显著性特征,并设计级联Transformer融合模块来学习示例间的多层时序相关性;然后利用多损失模型对融合特征进行多loss监督学习,以丰富预测的多样性;针对现有top-k的离散性问题,提出了带约束机制的滑窗top-k强化异常事件的相关性。在ShanghaiTech和UCF-Crime数据集上的对比实验与消融实验表明,DBF-CML-transMIL的异常检测曲线下面积(Area Under Curve,AUC)分别达到97.33%和83.82%;各模块都能有效提升视频异常事件检测的性能。展开更多
In this paper, a new method for discovering the candidate car license plate locations is presented. First, the image is decomposed using a Haar wavelet to get the HL band with vertical edges. Then, the HL band image i...In this paper, a new method for discovering the candidate car license plate locations is presented. First, the image is decomposed using a Haar wavelet to get the HL band with vertical edges. Then, the HL band image is binarized using an Otsu threshold. Next a black top-hat algorithm is applied to reduce the effects of interfering large continuous features other than the license plate. At this time, a moving window based modified variance score calculation is made for areas with white pixels. This work found that the top 3 detected rectangle windows correctly locate the license plate regions with a success rate of about 98.2%. Moreover, the proposed method is robust enough to locate the plates in cases where the rough vehicle position has not been previously discovered and the cars are not centered in the image.展开更多
文摘针对视频异常事件的时空相关性学习对检测性能存在重要影响的问题,提出了基于融合双支特征的带约束损失的视频异常检测方法(Dual-branch Feature Fusion Based Constrained Multi-loss Video Anomaly Detection,DBF-CML-transMIL)。该方法考虑多示例学习中片段的显著性和相关性,利用多层线性神经网络学习各片段的空间显著性特征,并设计级联Transformer融合模块来学习示例间的多层时序相关性;然后利用多损失模型对融合特征进行多loss监督学习,以丰富预测的多样性;针对现有top-k的离散性问题,提出了带约束机制的滑窗top-k强化异常事件的相关性。在ShanghaiTech和UCF-Crime数据集上的对比实验与消融实验表明,DBF-CML-transMIL的异常检测曲线下面积(Area Under Curve,AUC)分别达到97.33%和83.82%;各模块都能有效提升视频异常事件检测的性能。
文摘In this paper, a new method for discovering the candidate car license plate locations is presented. First, the image is decomposed using a Haar wavelet to get the HL band with vertical edges. Then, the HL band image is binarized using an Otsu threshold. Next a black top-hat algorithm is applied to reduce the effects of interfering large continuous features other than the license plate. At this time, a moving window based modified variance score calculation is made for areas with white pixels. This work found that the top 3 detected rectangle windows correctly locate the license plate regions with a success rate of about 98.2%. Moreover, the proposed method is robust enough to locate the plates in cases where the rough vehicle position has not been previously discovered and the cars are not centered in the image.
